The present invention relates to the analysis of activities in videos, and more particularly to accurately determining and distinguishing object movements and activities represented thereby.
Video surveillance enables object monitoring through video displays of one or more areas remote from a human monitor. Exemplary applications include security surveillance of public and private areas, for example parking lots for human and vehicle movements, assembly areas such as train stations and entertainment halls for abandoned baggage or objects, borders and doorways for unauthorized entry, secured areas for unauthorized vehicle or object movements and removals, etc. However, human visual attention may be ineffective, particularly for large volumes of video data. Due to many factors, illustratively including an infrequency of activities of interest, a fundamental tedium associated with the task and poor reliability in object tracking in environments with visual clutter and other distractions, human video surveillance may be both expensive and ineffective. Accordingly, it is often desirable to implement automated systems for video analysis.
Automated analysis of videos for determining object movements, activities and behaviors in video surveillance system data is known, wherein computers or other programmable devices directly analyze video data and attempt to determine the occurrence of activities of concern, for example to detect and distinguish abandoned objects within a scene such as packages and parked cars. However, determining and differentiating humans and objects or their movements within a video stream is often not reliable in realistic, real-world environments and applications, sometimes due to clutter, poor or variable lighting and object resolutions and distracting competing visual information.